[2604.08586] FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes

Computer Science > Machine Learning arXiv:2604.08586 (cs) [Submitted on 30 Mar 2026] Title:FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes Authors:David Ramos, Lucas Lacasa, Fermín Gutiérrez, Eusebio Valero, Gonzalo Rubio View a PDF of the paper titled FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes, by David Ramos and 3 other authors View PDF HTML (experimental) Abstract:Computational fluid dynamics (CFD) provides high-fidelity simulations of fluid flows but remains computationally expensive for many-query applications. In recent years deep learning (DL) has been used to construct data-driven fluid-dynamic surrogate models. In this work we consider a different learning paradigm and embrace generative modelling as a framework for constructing scalable fluid-dynamics surrogate models. We introduce FluidFlow, a generative model based on conditional flow-matching, a recent alternative to diffusion models that learns deterministic transport maps between noise and data distributions. FluidFlow is specifically designed to operate directly on CFD data defined on both structured and unstructured meshes alike, without the needs to perform any mesh interpolation pre-processing and preserving geometric fidelity. We assess the capabilities of FluidFlow using two different core neural network architectures, a U-Net and diffusion transformer (DiT), and condition their learning on physically...